Parts remain a critical missing element in the service equation for most customer service teams, negatively impacting service KPIs. No matter how efficient their contact center agents and technicians are, if the right parts are not available, resolutions are delayed and customers are disappointed.
Our client, a leading home appliance OEM, manufactures and supports over 3,200 appliance models and ships around 28.2 million parts and accessories in the US annually.
Challenges
The company had well established service management systems, tools and processes. However, parts remained a critical missing element in the service equation, negatively impacting service KPIs. No matter how efficient the agents and technicians were, if the right parts weren’t available, issues couldn’t be fixed the first time.
The high complexity and specialized nature of parts data, including parts BoMs, service records, and warranty claims, often residing in disparate systems made it challenging for the OEM to identify and provide the correct parts needed for each service visit, ultimately limiting the company’s ability to improve first-time fix rates (FTFR) and customer satisfaction.
Even with optimized service operations, the company had hit a ceiling on improving FTFRs as technicians continued to arrive on-site without the right parts. The core challenge wasn’t merely the availability of parts, but precisely identifying the correct parts to carry to fix the issue. Too often, this process relied on guesswork.
The business impact of this challenge was significant:
- Stagnant FTFR: This meant the company was stuck at an FTFR of between 75-80%, an inefficient rate for a leading global OEM.
- Multiple visits: Roughly 25-30% of service calls required multiple truck rolls due to lack of the right parts.
- Low productivity: Repeat visits were time consuming, affecting tech productivity and eroding profitability.
These challenges negatively affected customer satisfaction (CSAT) scores and brand equity.
Solution implemented: Parts AI Agent
The solution was to deploy the Bruviti Parts Agent to combine various data sources across the OEM’s systems.
Then use the combined data to power specialized parts algorithms that accurately identify necessary parts to fix each issue. By focusing on the ‘issue’ as the key to linking all data, the models could pinpoint the right parts needed with high accuracy and confidence.
- Connecting complex disparate data
Using NLP augmented by LLMs, the Parts Al Agent integrated and harmonized diverse data sources like parts BoMs, service records, and warranty claims. To transform them into a unified, usable format to feed the predictive parts models. - Specialized predictive algorithms
The Parts Al Agent used pre-trained neural networks to rapidly build predictive algorithms specifically tuned for parts, using extensive datasets unique to the customer. This process enabled it to precisely identify the parts needed for each service issue right out of the box. - Augmented scoring automation
Augmented scoring used by the Parts Al Agent would automatically optimize score performance, allowing the Agent to autonomously adapt and learn from new data and make recommendations to continually enhance the results for the company. - Secure, integrated deployment
Embedded in the customer’s secure enterprise ecosystem and service applications, the Parts Al Agent was deployed within existing call center and field operations. This plug-in flexibility ensured rapid deployment, with minimal training across the service organization.
Results with Bruviti Parts Agent
Conclusion
The Parts Agent AI focused on the issue to accurately identify the necessary parts for repairs. By significantly improving parts identification accuracy, Bruviti transformed the OEM’s service operations, resulting in better-prepared technicians, increased FTFR, fewer truck rolls, and improved customer satisfaction.
